{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# yellowbrick.features.jointplot\n",
    "# Implementations of joint plots for univariate and bivariate analysis.\n",
    "#\n",
    "# Author:   Prema Damodaran Roman \n",
    "# Created:  \n",
    "#\n",
    "# Copyright (C) 2017 District Data Labs\n",
    "# For license information, see LICENSE.txt\n",
    "#\n",
    "# ID: jointplot.py\n",
    "\n",
    "##########################################################################\n",
    "## Imports\n",
    "##########################################################################\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from yellowbrick.features.base import FeatureVisualizer\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "from yellowbrick.bestfit import draw_best_fit\n",
    "from yellowbrick.utils import is_dataframe\n",
    "\n",
    "class JointPlotVisualizer(FeatureVisualizer):\n",
    "    \"\"\"\n",
    "    JointPlotVisualizer allows for a simultaneous visualization of the relationship\n",
    "    between two variables and the distrbution of each individual variable.  The \n",
    "    relationship is plotted along the joint axis and univariate distributions\n",
    "    are plotted on top of the x axis and to the right of the y axis.\t\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, ax=None, feature=None, target=None, \n",
    "                 joint_plot='scatter', joint_args=None, \n",
    "                 xy_plot='hist', xy_args=None,\n",
    "                 size=6, ratio=5, space=.2, **kwargs):\n",
    "\n",
    "        \"\"\"\n",
    "        Initialize the visualization with many of the options required\n",
    "        in order to make most visualizations work.\n",
    "\n",
    "        These parameters can be influenced later on in the visualization\n",
    "        process, but can and should be set as early as possible.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "\n",
    "        ax: This is inherited from FeatureVisualizer but is defined within\n",
    "            JointPlotVisualizer since there are three axes objects.\n",
    "\n",
    "        feature: The name of the X variable\n",
    "            If a DataFrame is passed to fit and feature is None, feature\n",
    "            is selected as the column of the DataFrame.  There must be only\n",
    "            one column in the DataFrame.\n",
    "\n",
    "        target: The name of the Y variable\n",
    "            If target is None and a y value is passed to fit then the target\n",
    "            is selected from the target vector.\n",
    "\n",
    "        joint_plot: The type of plot to render in the joint axis.  Currently,\n",
    "            the choices are scatter and hex.  Use scatter for small datasets\n",
    "            and hex for large datasets\n",
    "\n",
    "        joint_args: Keyword arguments used for customizing the joint plot.\n",
    "                Property        Description\n",
    "                alpha           transparency\n",
    "                facecolor       background color of the joint axis\n",
    "                aspect          aspect ratio\n",
    "                fit             used if scatter is selected for joint_plot to draw a \n",
    "                                best fit line - values can be True or False.\n",
    "                                Uses Yellowbrick.bestfit\n",
    "                estimator       used if scatter is selected for joint_plot to determine\n",
    "                                the type of best fit line to use.  Refer to \n",
    "                                Yellowbrick.bestfit for types of estimators that can be used.\n",
    "                x_bins          used if hex is selected to set the number of bins for the x value\n",
    "                y_bins          used if hex is selected to set the number of bins for the y value\n",
    "                cmap            string or matplotlib cmap to colorize lines\n",
    "                                Use either color to colorize the lines on a per class basis or\n",
    "                                colormap to color them on a continuous scale.\n",
    "\n",
    "\n",
    "        xy_plot: The type of plot to render along the x and y axes.\n",
    "            Currently, the choice is hist\n",
    "\n",
    "        xy_args: Keyword arguments used for customizing the x and y plots.\n",
    "                Property        Description\n",
    "                alpha           transparency\n",
    "                facecolor_x     background color of the x axis\n",
    "                facecolor_y     background color of the y axis\n",
    "                bins            used to set up the number of bins for the hist plot\n",
    "                histcolor_x     used to set the color for the histogram on the x axis\n",
    "                histcolor_y     used to set the color for the histogram on the y axis\n",
    "\n",
    "        size: Size of each side of the figure in inches.\n",
    "\n",
    "        ratio: Ratio of joint axis size to the x and y axes height.\n",
    "\n",
    "        space: Space between the joint axis and the x and y axes.\n",
    "\n",
    "        kwargs: Keyword arguments passed to the super class.\n",
    "\n",
    "        \"\"\"\n",
    "        \n",
    "        #check matplotlib version - needs to be version 2.0.0\t\n",
    "        if mpl.__version__ == '2.0.0':\n",
    "            pass \n",
    "        else:\n",
    "            print('This Visualizer requires Matplotlib version 2.0.0. Please upgrade to  continue.')\n",
    "\n",
    "        super(JointPlotVisualizer, self).__init__(ax, **kwargs)\n",
    "\n",
    "        self.feature = feature\n",
    "        self.target = target\n",
    "        self.joint_plot = joint_plot\n",
    "        self.joint_args = joint_args\n",
    "        self.xy_plot = xy_plot\n",
    "        self.xy_args = xy_args\n",
    "        self.size = size\n",
    "        self.ratio = ratio\n",
    "        self.space = space\n",
    "\n",
    "    def fit(self, X, y, **kwargs):\n",
    "   \n",
    "        \"\"\"\n",
    "\n",
    "        Sets up the X and y variables for the jointplot\n",
    "        and checks to ensure that X and y are of the \n",
    "        correct data type\n",
    "\n",
    "        Fit calls draw \n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "\n",
    "        X : ndarray or DataFrame of shape n x 1\n",
    "            A matrix of n instances with 1 feature\n",
    "\n",
    "        y : ndarray or Series of length n\n",
    "            An array or series of the target value\n",
    "\n",
    "        kwargs: dict\n",
    "            keyword arguments passed to Scikit-Learn API.\n",
    "        \"\"\"\n",
    "\n",
    "        #throw an error if X has more than 1 column\n",
    "        if is_dataframe(X):\n",
    "            nrows, ncols = X.shape\n",
    "            if ncols > 1:\n",
    "                raise YellowbrickValueError(\n",
    "                \"X needs to be an ndarray or DataFrame with one feature, please select one feature from the DataFrame\"\n",
    "                )   \n",
    "\n",
    "        #throw an error is y is None\n",
    "        if y is None:\n",
    "            raise YellowbrickValueError(\n",
    "                \"Joint plots are useful for classification and regression problems, which            require a target variable\")\n",
    "\n",
    "\n",
    "        # Handle the feature name if it is None.\n",
    "        if self.feature is None:\n",
    "\n",
    "        # If X is a data frame, get the columns off it.\n",
    "        if is_dataframe(X):\n",
    "            self.feature = X.columns\n",
    "\n",
    "        else:\n",
    "            self.feature = ['x']\n",
    "\n",
    "        # Handle the target name if it is None.\n",
    "        if self.target is None:\n",
    "            self.target = ['y']\n",
    "\n",
    "        self.draw(X, y, **kwargs)\n",
    "        return self\n",
    "\n",
    "\n",
    "    def draw(self, X, y, **kwargs):  \n",
    "\n",
    "        \"\"\"       \n",
    "        Sets up the layout for the joint plot\n",
    "        draw calls draw_joint and draw_xy\n",
    "        to render the visualizations\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        fig = plt.figure(figsize=(self.size, self.size))\n",
    "        gs = plt.GridSpec(self.ratio + 1, self.ratio + 1)\n",
    "\n",
    "        #Set up the 3 axes objects\n",
    "        joint_ax = fig.add_subplot(gs[1:, :-1])\n",
    "        x_ax = fig.add_subplot(gs[0, :-1], sharex=joint_ax)\n",
    "        y_ax = fig.add_subplot(gs[1:, -1], sharey=joint_ax)\n",
    "\n",
    "        fig.tight_layout()\n",
    "        fig.subplots_adjust(hspace=self.space, wspace=self.space)\n",
    "\n",
    "        self.fig = fig\n",
    "        self.joint_ax = joint_ax\n",
    "        self.x_ax = x_ax\n",
    "        self.y_ax = y_ax\n",
    "\n",
    "        self.draw_joint(X, y, **kwargs)\n",
    "        self.draw_xy(X, y, **kwargs)\n",
    " \n",
    "    def draw_joint(self, X, y, **kwargs):\n",
    "\n",
    "        \"\"\"       \n",
    "        Draws the visualization for the joint axis\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        if self.joint_args is None:\n",
    "            self.joint_args = {}\n",
    "\n",
    "        self.joint_args.setdefault(\"alpha\", 0.4)\n",
    "        facecolor = self.joint_args.pop(\"facecolor\", \"#dddddd\")\n",
    "        self.joint_ax.set_facecolor(facecolor)\n",
    "\n",
    "        if self.joint_plot == \"scatter\":\n",
    "            aspect = self.joint_args.pop(\"aspect\", \"auto\")\n",
    "            self.joint_ax.set_aspect(aspect)\n",
    "            self.joint_ax.scatter(X, y, **self.joint_args)\n",
    "\n",
    "            fit = self.joint_args.pop(\"fit\", True)\n",
    "            if fit:\n",
    "                estimator = self.joint_args.pop(\"estimator\", \"linear\")\n",
    "                draw_best_fit(X, y, self.joint_ax, estimator)\n",
    "\n",
    "        elif self.joint_plot == \"hex\":\n",
    "            x_bins = self.joint_args.pop(\"x_bins\", 50)\n",
    "            y_bins = self.joint_args.pop(\"y_bins\", 50)\n",
    "            colormap = self.joint_args.pop(\"cmap\", 'Blues')\n",
    "            gridsize = int(np.mean([x_bins, y_bins]))\n",
    "\n",
    "            xmin = X.min()\n",
    "            xmax = X.max()\n",
    "            ymin = y.min()\n",
    "            ymax = y.max()\n",
    "            \n",
    "            self.joint_ax.hexbin(X, y, gridsize=gridsize, cmap=colormap, \n",
    "                                 mincnt=1, **self.joint_args)\n",
    "            self.joint_ax.axis([xmin, xmax, ymin, ymax])\n",
    "\n",
    "    def draw_xy(self, X, y, **kwargs):\n",
    "\n",
    "        \"\"\"       \n",
    "        Draws the visualization for the x and y axes\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        if self.xy_args is None:\n",
    "            self.xy_args = {}\n",
    "\n",
    "        facecolor_x = self.xy_args.pop(\"facecolor_x\", \"#dddddd\")\n",
    "        self.x_ax.set_facecolor(facecolor_x)\n",
    "        facecolor_y = self.xy_args.pop(\"facecolor_y\", \"#dddddd\")\n",
    "        self.y_ax.set_facecolor(facecolor_y)\n",
    "\n",
    "        \n",
    "        if self.xy_plot == \"hist\":\n",
    "            hist_bins = self.xy_args.pop(\"bins\", 50)\n",
    "            self.xy_args.setdefault(\"alpha\", 0.4)\n",
    "            histcolor_x = self.xy_args.pop(\"histcolor_x\", \"#6897bb\")\n",
    "            self.x_ax.set_facecolor(facecolor_x)\n",
    "            histcolor_y = self.xy_args.pop(\"histcolor_y\", \"#6897bb\")\n",
    "            self.y_ax.set_facecolor(facecolor_y)\n",
    "            self.x_ax.hist(X, bins=hist_bins, color=histcolor_x, **self.xy_args)\n",
    "            self.y_ax.hist(y, bins=hist_bins, color=histcolor_y, \n",
    "                           orientation='horizontal', **self.xy_args)\n",
    " \n",
    "    def show(self, **kwargs):\n",
    "\n",
    "        \"\"\"       \n",
    "        Creates the labels for the feature and target variables\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        self.joint_ax.set_xlabel(self.feature)\n",
    "        self.joint_ax.set_ylabel(self.target)\n",
    "        self.finalize(**kwargs)\n",
    "\n",
    "    def finalize(self, **kwargs):\n",
    "\n",
    "        \"\"\"\n",
    "        Finalize executes any subclass-specific axes finalization steps.\n",
    "        The user calls show and show calls finalize.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        kwargs: generic keyword arguments.\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        plt.setp(self.x_ax.get_xticklabels(), visible=False)\n",
    "        plt.setp(self.y_ax.get_yticklabels(), visible=False)\n",
    "\n",
    "        plt.setp(self.x_ax.yaxis.get_majorticklines(), visible=False)\n",
    "        plt.setp(self.x_ax.yaxis.get_minorticklines(), visible=False)\n",
    "        plt.setp(self.y_ax.xaxis.get_majorticklines(), visible=False)\n",
    "        plt.setp(self.y_ax.xaxis.get_minorticklines(), visible=False)\n",
    "        plt.setp(self.x_ax.get_yticklabels(), visible=False)\n",
    "        plt.setp(self.y_ax.get_xticklabels(), visible=False)\n",
    "        self.x_ax.yaxis.grid(False)\n",
    "        self.y_ax.xaxis.grid(False)\n",
    "        self.fig.suptitle(\"Joint Plot of {} vs {}\"\n",
    "                .format(self.feature, self.target), y=1.05)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys  \n",
    "import pandas as pd\n",
    "\n",
    "## The path to the test data sets\n",
    "FIXTURES  = os.path.join(os.getcwd(), \"data\")\n",
    "\n",
    "## Dataset loading mechanisms\n",
    "datasets = {\n",
    "    \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n",
    "    \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n",
    "    \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n",
    "}\n",
    "\n",
    "\n",
    "def load_data(name):\n",
    "    \"\"\"\n",
    "    Loads and wrangles the passed in dataset by name.\n",
    "    If download is specified, this method will download any missing files. \n",
    "    \"\"\"\n",
    "    \n",
    "    # Get the path from the datasets \n",
    "    path = datasets[name]\n",
    "        \n",
    "    # Return the data frame\n",
    "    return pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = load_data('concrete')\n",
    "features = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n",
    "feature = 'cement'\n",
    "target = 'strength'\n",
    "# Get the X and y data from the DataFrame \n",
    "X = df[feature].as_matrix()\n",
    "y = df[target].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAHVCAYAAABVBTRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108820b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot=\"hex\")\n",
    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
    "g = visualizer.show()    # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
